# Copyright 2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
from tests.mark_utils import arg_mark

import numpy as np

import mindspore.common.dtype as mstype
from mindspore import context
from mindspore.common.tensor import Tensor
from mindspore.nn import Cell
from mindspore.ops import operations as P


class Net(Cell):
    def __init__(self, dtype):
        super().__init__()
        self.cast = P.Cast()
        self.dtype = dtype
        self.unique = P.Unique()
        self.gather = P.Gather()
        self.indices = Tensor([0, 1, 2])

    def construct(self, x):
        unique_indices, _ = self.unique(self.indices)
        x = self.gather(x, unique_indices, 0)
        return self.cast(x, self.dtype)


@arg_mark(plat_marks=['cpu_linux', 'cpu_windows', 'cpu_macos'], level_mark='level1', card_mark='onecard',
          essential_mark='unessential')
def test_cast_bool():
    """
    Feature: test cast op
    Description: test the ops in dynamic shape
    Expectation: expect correct shape result.
    """
    tensor_to_cast = []
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.bool_)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float64)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int8)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int64)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint8)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint64)))
    t = mstype.bool_
    context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
    for tensor in tensor_to_cast:
        net = Net(t)
        output = net(tensor)
        assert output.asnumpy().shape == (3, 2)


@arg_mark(plat_marks=['cpu_linux', 'cpu_windows', 'cpu_macos'], level_mark='level1', card_mark='onecard',
          essential_mark='unessential')
def test_cast_float16():
    """
    Feature: test cast op
    Description: test the ops in dynamic shape
    Expectation: expect correct shape result.
    """
    tensor_to_cast = []
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.bool_)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float64)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int8)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int64)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint8)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint64)))
    t = mstype.float16

    context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
    for tensor in tensor_to_cast:
        net = Net(t)
        output = net(tensor)
        assert output.asnumpy().shape == (3, 2)


@arg_mark(plat_marks=['cpu_linux', 'cpu_windows', 'cpu_macos'], level_mark='level1', card_mark='onecard',
          essential_mark='unessential')
def test_cast_float32():
    """
    Feature: test cast op
    Description: test the ops in dynamic shape
    Expectation: expect correct shape result.
    """
    tensor_to_cast = []
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.bool_)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float64)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int8)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int64)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint8)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint64)))
    t = mstype.float32

    context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
    for tensor in tensor_to_cast:
        net = Net(t)
        output = net(tensor)
        assert output.asnumpy().shape == (3, 2)


@arg_mark(plat_marks=['cpu_linux', 'cpu_windows', 'cpu_macos'], level_mark='level1', card_mark='onecard',
          essential_mark='unessential')
def test_cast_float64():
    """
    Feature: test cast op
    Description: test the ops in dynamic shape
    Expectation: expect correct shape result.
    """
    tensor_to_cast = []
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.bool_)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float64)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int8)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int64)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint8)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint64)))
    t = mstype.float64

    context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
    for tensor in tensor_to_cast:
        net = Net(t)
        output = net(tensor)
        assert output.asnumpy().shape == (3, 2)


@arg_mark(plat_marks=['cpu_linux', 'cpu_windows', 'cpu_macos'], level_mark='level1', card_mark='onecard',
          essential_mark='unessential')
def test_cast_int8():
    """
    Feature: test cast op
    Description: test the ops in dynamic shape
    Expectation: expect correct shape result.
    """
    tensor_to_cast = []
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.bool_)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float64)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int8)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int64)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint8)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint64)))
    t = mstype.int8

    context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
    for tensor in tensor_to_cast:
        net = Net(t)
        output = net(tensor)
        assert output.asnumpy().shape == (3, 2)


@arg_mark(plat_marks=['cpu_linux', 'cpu_windows', 'cpu_macos'], level_mark='level1', card_mark='onecard',
          essential_mark='unessential')
def test_cast_int16():
    """
    Feature: test cast op
    Description: test the ops in dynamic shape
    Expectation: expect correct shape result.
    """
    tensor_to_cast = []
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.bool_)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float64)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int8)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int64)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint8)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint64)))
    t = mstype.int16

    context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
    for tensor in tensor_to_cast:
        net = Net(t)
        output = net(tensor)
        assert output.asnumpy().shape == (3, 2)


@arg_mark(plat_marks=['cpu_linux', 'cpu_windows', 'cpu_macos'], level_mark='level1', card_mark='onecard',
          essential_mark='unessential')
def test_cast_int32():
    """
    Feature: test cast op
    Description: test the ops in dynamic shape
    Expectation: expect correct shape result.
    """
    tensor_to_cast = []
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.bool_)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float64)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int8)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int64)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint8)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint64)))
    t = mstype.int32

    context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
    for tensor in tensor_to_cast:
        net = Net(t)
        output = net(tensor)
        assert output.asnumpy().shape == (3, 2)


@arg_mark(plat_marks=['cpu_linux', 'cpu_windows', 'cpu_macos'], level_mark='level1', card_mark='onecard',
          essential_mark='unessential')
def test_cast_int64():
    """
    Feature: test cast op
    Description: test the ops in dynamic shape
    Expectation: expect correct shape result.
    """
    tensor_to_cast = []
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.bool_)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float64)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int8)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int64)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint8)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint64)))
    t = mstype.int64

    context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
    for tensor in tensor_to_cast:
        net = Net(t)
        output = net(tensor)
        assert output.asnumpy().shape == (3, 2)


@arg_mark(plat_marks=['cpu_linux', 'cpu_windows', 'cpu_macos'], level_mark='level1', card_mark='onecard',
          essential_mark='unessential')
def test_cast_uint8():
    """
    Feature: test cast op
    Description: test the ops in dynamic shape
    Expectation: expect correct shape result.
    """
    tensor_to_cast = []
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.bool_)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float64)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int8)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int64)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint8)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint64)))
    t = mstype.uint8

    context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
    for tensor in tensor_to_cast:
        net = Net(t)
        output = net(tensor)
        assert output.asnumpy().shape == (3, 2)


@arg_mark(plat_marks=['cpu_linux', 'cpu_windows', 'cpu_macos'], level_mark='level1', card_mark='onecard',
          essential_mark='unessential')
def test_cast_uint16():
    """
    Feature: test cast op
    Description: test the ops in dynamic shape
    Expectation: expect correct shape result.
    """
    tensor_to_cast = []
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.bool_)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float64)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int8)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int64)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint8)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint64)))
    t = mstype.uint16

    context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
    for tensor in tensor_to_cast:
        net = Net(t)
        output = net(tensor)
        assert output.asnumpy().shape == (3, 2)


@arg_mark(plat_marks=['cpu_linux', 'cpu_windows', 'cpu_macos'], level_mark='level1', card_mark='onecard',
          essential_mark='unessential')
def test_cast_uint32():
    """
    Feature: test cast op
    Description: test the ops in dynamic shape
    Expectation: expect correct shape result.
    """
    tensor_to_cast = []
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.bool_)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float64)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int8)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int64)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint8)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint64)))
    t = mstype.uint32

    context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
    for tensor in tensor_to_cast:
        net = Net(t)
        output = net(tensor)
        assert output.asnumpy().shape == (3, 2)


@arg_mark(plat_marks=['cpu_linux', 'cpu_windows', 'cpu_macos'], level_mark='level1', card_mark='onecard',
          essential_mark='unessential')
def test_cast_uint64():
    """
    Feature: test cast op
    Description: test the ops in dynamic shape
    Expectation: expect correct shape result.
    """
    tensor_to_cast = []
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.bool_)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.float64)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int8)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.int64)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint8)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint16)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint32)))
    tensor_to_cast.append(Tensor(np.random.uniform(-2, 2, (3, 2)).astype(np.uint64)))
    t = mstype.uint64

    context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
    for tensor in tensor_to_cast:
        net = Net(t)
        output = net(tensor)
        assert output.asnumpy().shape == (3, 2)
